The trend of the quality of cause-of-death data and its association with socio-economic indicators in Serbia in the period 2005-19.

This study aims to evaluate the temporal trend in the quality of cause-of-death data and garbage code profiles and to determine its association with socio-economic status in Serbia. A longitudinal study was assessed using data from mortality registers from 2005 to 2019. Computer application Analysis of Causes of National Deaths for Action (ANACONDA) calculates the distribution of garbage codes by severity and composite quality indicator: Vital Statistics Performance Index for Quality (VSPI(Q)). A relationship between VSPI(Q) and country development was estimated by analysing two socio-economic indicators: the Socio-demographic Index and the Human Development Index (HDI). Serbia indicates progress in strengthening cause-of-death statistics. The steady upward trend of the VSPI(Q) index has risen from 55.6 (medium quality) to 70.2 (high quality) over the examined years. Significant reduction of 'Insufficiently specified causes with limited impact' (Level 4) and an increase in the trend of 'High-impact garbage codes' (Levels 1 to 3) were evident. Decreased deaths of no policy value (annual percentage change of -1.41%) have manifested since 2014. A strong positive association between VSPI(Q) and socio-economic indicators was assessed, where the HDI has shown a stronger association with VSPI(Q). Improved socio-economic conditions on the national level are followed by enhanced cause-of-death data quality. Upcoming actions to improve quality should be directed at high-impact garbage codes. The study underlines the need to prioritise the education and training of physicians with a crucial role in death certification to overcome many cause-of-death quality issues identified in this assessment.

Assessing the quality of emergency department data for injury surveillance in Victoria, Australia: a comparative analysis of two Victorian hospital data sources.

OBJECTIVE: The emergency department (ED) is pivotal in treating serious injuries, making it a valuable source for population-based injury surveillance. In Victoria, information that is relevant to injury surveillance is collected in the Victorian Emergency Minimum Dataset (VEMD). This study aims to assess the data quality of the VEMD as an injury data source by comparing it with the Victorian Admitted Episodes Dataset (VAED). DESIGN: A retrospective observational study of administrative healthcare data. SETTING AND PARTICIPANTS: VEMD and VAED data from July 2014 to June 2019 were compared. Including only hospitals contributing to both datasets, cases that (1) arrived at the ED and (2) were subsequently admitted, were selected. RESULTS: While the overall number of cases was similar, VAED outnumbered VEMD cases (414 630 vs 404 608), suggesting potential under-reporting of injuries in the ED. Age-related differences indicated a relative under-representation of older individuals in the VEMD. Injuries caused by falls or transport, and intentional injuries were relatively under-reported in the VEMD. CONCLUSIONS: Injury cases were more numerous in the VAED than in the VEMD even though the number is expected to be equal based on case selection. Older patients were under-represented in the VEMD; this could partly be attributed to patients being admitted for an injury after they presented to the ED with a non-injury ailment. The patterns of under-representation described in this study should be taken into account in ED-based injury incidence reporting.

Data quality assessment of the Enhanced Gonococcal Antimicrobial Surveillance Programme (EGASP), Thailand, 2015-2021.

BACKGROUND: Quality assessments of gonococcal surveillance data are critical to improve data validity and to enhance the value of surveillance findings. Detecting data errors by systematic audits identifies areas for quality improvement. We designed and implemented an internal audit process to evaluate the accuracy and completeness of surveillance data for the Thailand Enhanced Gonococcal Antimicrobial Surveillance Programme (EGASP). METHODS: We conducted a data quality audit of source records by comparison with the data stored in the EGASP database for five audit cycles from 2015-2021. Ten percent of culture-confirmed cases of Neisseria gonorrhoeae were randomly sampled along with any cases identified with elevated antimicrobial susceptibility testing results and cases with repeat infections. Incorrect and incomplete data were investigated, and corrective action and preventive actions (CAPA) were implemented. Accuracy was defined as the percentage of identical data in both the source records and the database. Completeness was defined as the percentage of non-missing data from either the source document or the database. Statistical analyses were performed using the t-test and the Fisher's exact test. RESULTS: We sampled and reviewed 70, 162, 85, 68, and 46 EGASP records during the five audit cycles. Overall accuracy and completeness in the five audit cycles ranged from 93.6% to 99.4% and 95.0% to 99.9%, respectively. Overall, completeness was significantly higher than accuracy (p = 0.017). For each laboratory and clinical data element, concordance was >85% in all audit cycles except for two laboratory data elements in two audit cycles. These elements significantly improved following identification and CAPA implementation. DISCUSSION: We found a high level of data accuracy and completeness in the five audit cycles. The implementation of the audit process identified areas for improvement. Systematic quality assessments of laboratory and clinical data ensure high quality EGASP surveillance data to monitor for antimicrobial resistant Neisseria gonorrhoeae in Thailand.

The National Swedish Lymphoma Register - a systematic validation of data quality.

BACKGROUND AND PURPOSE: The Swedish Lymphoma Register (SLR) was initiated in the year 2000 with the aim to monitor quality of care in diagnostics, treatment and outcome of all lymphomas diagnosed nationally among adults. Here, we present the first systematic validation of SLR records as a basis for improved register quality and patient care. PATIENTS AND METHODS: We evaluated timeliness and completeness of register records among patients diagnosed with lymphoma in the SLR (n = 16,905) compared with the National Cancer Register for the period 2013-2020. Comparability was assessed through evaluation of coding routines against national and international guidelines. Accuracy of 42 variables was evaluated through re-abstraction of data from medical records among 600 randomly selected patients diagnosed in 2016-2017 and treated across all six Swedish healthcare regions.  Results: Completeness was high, >95% per year for the period 2013-2018, and >89% for 2019-2020 compared to the National Cancer Register. One in four patients was registered within 3 months, and 89.9% within 2 years of diagnosis. Registration instructions and coding procedures followed the prespecified guidelines. Missingness was generally low (<5%), but high for occasional variables, for example, those describing maintenance and consolidative treatment. Exact agreement of categorical variables was high overall (>80% for 24/34 variables), especially for treatment-related data (>80% for 17/19 variables). INTERPRETATION: Completeness and accuracy are high in the SLR, while timeliness could be improved. Finetuning of variable registration guided by this validation can further improve reliability of register reports and advance service to lymphoma patients and health care in the future.

Characterizing physician directory data quality: variation by specialty, state, and insurer.

BACKGROUND: As U.S. legislators are urged to combat ghost networks in behavioral health and address the provider data quality issue, it becomes important to better characterize the variation in data quality of provider directories to understand root causes and devise solutions. Therefore, this manuscript examines consistency of address, phone number, and specialty information for physician entries from 5 national health plan provider directories by insurer, physician specialty, and state. METHODS: We included all physicians in the Medicare Provider Enrollment, Chain, and Ownership System (PECOS) found in ≥ 2 health insurer physician directories across 5 large national U.S. health insurers. We examined variation in consistency of address, phone number, and specialty information among physicians by insurer, physician specialty, and state. RESULTS: Of 634,914 unique physicians in the PECOS database, 449,282 were found in ≥ 2 directories and included in our sample. Across insurers, consistency of address information varied from 16.5 to 27.9%, consistency of phone number information varied from 16.0 to 27.4%, and consistency of specialty information varied from 64.2 to 68.0%. General practice, family medicine, plastic surgery, and dermatology physicians had the highest consistency of addresses (37-42%) and phone numbers (37-43%), whereas anesthesiology, nuclear medicine, radiology, and emergency medicine had the lowest consistency of addresses (11-21%) and phone numbers (9-14%) across health insurer directories. There was marked variation in consistency of address, phone number, and specialty information by state. CONCLUSIONS: In evaluating a large national sample of U.S. physicians, we found minimal variation in provider directory consistency by insurer, suggesting that this is a systemic problem that insurers have not solved, and considerable variation by physician specialty with higher quality data among more patient-facing specialties, suggesting that physicians may respond to incentives to improve data quality. These data highlight the importance of novel policy solutions that leverage technology targeting data quality to centralize provider directories so as not to not reinforce existing data quality issues or policy solutions to create national and state-level standards that target both insurers and physician groups to maximize quality of provider information.

Data quality and timeliness analysis for post-vaccination adverse event cases reported through healthcare data exchange to FDA BEST pilot platform.

Introduction: This study is part of the U.S. Food and Drug Administration (FDA)'s Biologics Effectiveness and Safety (BEST) initiative, which aims to improve the FDA's postmarket surveillance capabilities by using real-world data (RWD). In the United States, using RWD for postmarket surveillance has been hindered by the inability to exchange clinical data between healthcare providers and public health organizations in an interoperable format. However, the Office of the National Coordinator for Health Information Technology (ONC) has recently enacted regulation requiring all healthcare providers to support seamless access, exchange, and use of electronic health information through the interoperable HL7 Fast Healthcare Interoperability Resources (FHIR) standard. To leverage the recent ONC changes, BEST designed a pilot platform to query and receive the clinical information necessary to analyze suspected AEs. This study assessed the feasibility of using the RWD received through the data exchange of FHIR resources to study post-vaccination AE cases by evaluating the data volume, query response time, and data quality. Materials and methods: The study used RWD from 283 post-vaccination AE cases, which were received through the platform. We used descriptive statistics to report results and apply 322 data quality tests based on a data quality framework for EHR. Results: The volume analysis indicated the average clinical resources for a post-vaccination AE case was 983.9 for the median partner. The query response time analysis indicated that cases could be received by the platform at a median of 3 min and 30 s. The quality analysis indicated that most of the data elements and conformance requirements useful for postmarket surveillance were met. Discussion: This study describes the platform's data volume, data query response time, and data quality results from the queried postvaccination adverse event cases and identified updates to current standards to close data quality gaps.

[Managing routine health information system data during a security crisis in the health district of Timbuktu, Mali in 2023]. / La gestion des données du système d'information sanitaire de routine en période de crise sécuritaire dans le district sanitaire de Tombouctou, au Mali en 2023.

Introduction: an effective health information system (HIS) ensures the production, analysis, dissemination and use of reliable and up-to-date information on the determinants of health. However, it can encounter obstacles that hinder its functioning, such as armed conflicts, which limit access and quality of healthcare services. The purpose of our study was to help improve data management for routine health information system in the health district of Timbuktu during a security crisis. Methods: we conducted a descriptive cross-sectional study, among health information management professionals in the Timbuktu Health District from 15 April to 08 September 2023. Data obtained from a survey questionnaire were analyzed using Epi Info version 7.2.2. and processed using Microsoft Word and Excel 2016. Results: a total of 6 health facilities were surveyed. Data collection, analysis and feedback were very poor. Data quality was 100% complete, 92.40% prompt and 68.11% accurate. The major constraints were: low involvement of health workers in the SIS (22.22%), insufficient training on the SISR (29.63%), supervision (47.06%), internet inaccessibility (66.67%), feeling of insecurity (37.04%) and fear (61.76%) in health facilities. Conclusion: our results show low-level processes, poor network coverage, shortage of qualified health information management professionals and increasing insecurity. A broader mixed-methods research would provide a better understanding.

Data quality in Brazilian population-based cancer registries for gastrointestinal cancers.

BACKGROUND: Population-based cancer registries (PBCRs) are the primary source of information for cancer surveillance and monitoring. Currently, there are 30 active PBCRs in Brazil. The objective of this study was to analyze the data quality of five gastrointestinal cancers (esophagus, stomach, colorectal, liver, and pancreas) according to the criteria of comparability, validity, completeness, and timeliness in Brazilian cancer registries. METHODS: This study included data from Brazilian PBCRs with more than ten years of historical data starting in the year 2000, regardless of the type of defined geographical coverage (state, metropolitan region, or capital), totaling 16 registries. Brazilian PBCRs were evaluated based on four international data quality criteria: comparability, validity (accuracy), completeness, and timeliness. All cancer cases were analyzed, except for nonmelanoma skin cancer cases (C44) and five gastrointestinal tumors (esophageal cancer, stomach cancer, colorectal cancer, liver cancer, and pancreatic cancer) per cancer registry and sex, according to the available period. RESULTS: The 16 Brazilian PBCRs represent 17% of the population (36 million inhabitants in 2021) according to data from 2000 to 2018. There was a variation in the incidence in the historical series ranging from 12 to 19 years. The proportion of morphologically verified (MV%) cases varied from 74.3% (Manaus) to 94.8% (Aracaju), and the proportion of incidentally reported death certificate only (DCO%) cases varied from 3.0% (São Paulo) to 23.9% (Espírito Santo). High-lethality malignant neoplasms, such as liver and pancreas, had DCO percentages greater than 30% in most cancer registries. The sixteen registries have more than a 48-month delay in data release compared to the 2022 calendar year. CONCLUSION: The studied Brazilian cancer registries met international comparability criteria; however, half of the registries showed indices below the expected levels for validity and completeness criteria for high-lethality tumors such as liver and pancreas tumors, in addition to a long delay in data availability and disclosure. Significant efforts are necessary to ensure the operational and stability of the PBCR in Brazil, which continues to be a tool for monitoring cancer incidence and assessing national cancer control policies.

Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters.

Labeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radiographs and those with nodules obscured by ribs appear similar. Thus, high-quality datasets referred to chest computed tomography (CT) are required to prevent the misclassification of nodular chest radiographs as normal. From this perspective, a deep learning strategy employing chest radiography data with pixel-level annotations referencing chest CT scans may improve nodule detection and localization compared to image-level labels. We trained models using a National Institute of Health chest radiograph-based labeling dataset and an AI-HUB CT-based labeling dataset, employing DenseNet architecture with squeeze-and-excitation blocks. We developed four models to assess whether CT versus chest radiography and pixel-level versus image-level labeling would improve the deep learning model's performance to detect nodules. The models' performance was evaluated using two external validation datasets. The AI-HUB dataset with image-level labeling outperformed the NIH dataset (AUC 0.88 vs 0.71 and 0.78 vs. 0.73 in two external datasets, respectively; both p < 0.001). However, the AI-HUB data annotated at the pixel level produced the best model (AUC 0.91 and 0.86 in external datasets), and in terms of nodule localization, it significantly outperformed models trained with image-level annotation data, with a Dice coefficient ranging from 0.36 to 0.58. Our findings underscore the importance of accurately labeled data in developing reliable deep learning algorithms for nodule detection in chest radiography.

Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study.

BACKGROUND: Electronic health records (EHRs) play an increasingly important role in delivering HIV care in low- and middle-income countries. The data collected are used for direct clinical care, quality improvement, program monitoring, public health interventions, and research. Despite widespread EHR use for HIV care in African countries, challenges remain, especially in collecting high-quality data. OBJECTIVE: We aimed to assess data completeness, accuracy, and timeliness compared to paper-based records, and factors influencing data quality in a large-scale EHR deployment in Rwanda. METHODS: We randomly selected 50 health facilities (HFs) using OpenMRS, an EHR system that supports HIV care in Rwanda, and performed a data quality evaluation. All HFs were part of a larger randomized controlled trial, with 25 HFs receiving an enhanced EHR with clinical decision support systems. Trained data collectors visited the 50 HFs to collect 28 variables from the paper charts and the EHR system using the Open Data Kit app. We measured data completeness, timeliness, and the degree of matching of the data in paper and EHR records, and calculated concordance scores. Factors potentially affecting data quality were drawn from a previous survey of users in the 50 HFs. RESULTS: We randomly selected 3467 patient records, reviewing both paper and EHR copies (194,152 total data items). Data completeness was >85% threshold for all data elements except viral load (VL) results, second-line, and third-line drug regimens. Matching scores for data values were close to or >85% threshold, except for dates, particularly for drug pickups and VL. The mean data concordance was 10.2 (SD 1.28) for 15 (68%) variables. HF and user factors (eg, years of EHR use, technology experience, EHR availability and uptime, and intervention status) were tested for correlation with data quality measures. EHR system availability and uptime was positively correlated with concordance, whereas users' experience with technology was negatively correlated with concordance. The alerts for missing VL results implemented at 11 intervention HFs showed clear evidence of improving timeliness and completeness of initially low matching of VL results in the EHRs and paper records (11.9%-26.7%; P<.001). Similar effects were seen on the completeness of the recording of medication pickups (18.7%-32.6%; P<.001). CONCLUSIONS: The EHR records in the 50 HFs generally had high levels of completeness except for VL results. Matching results were close to or >85% threshold for nondate variables. Higher EHR stability and uptime, and alerts for entering VL both strongly improved data quality. Most data were considered fit for purpose, but more regular data quality assessments, training, and technical improvements in EHR forms, data reports, and alerts are recommended. The application of quality improvement techniques described in this study should benefit a wide range of HFs and data uses for clinical care, public health, and disease surveillance.

Multi-source heterogeneous blockchain data quality assessment model for enterprise business activities.

Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain.

Understanding the value of curation: A survey of US data repository curation practices and perceptions.

Data curators play an important role in assessing data quality and take actions that may ultimately lead to better, more valuable data products. This study explores the curation practices of data curators working within US-based data repositories. We performed a survey in January 2021 to benchmark the levels of curation performed by repositories and assess the perceived value and impact of curation on the data sharing process. Our analysis included 95 responses from 59 unique data repositories. Respondents primarily were professionals working within repositories and examined curation performed within a repository setting. A majority 72.6% of respondents reported that "data-level" curation was performed by their repository and around half reported their repository took steps to ensure interoperability and reproducibility of their repository's datasets. Curation actions most frequently reported include checking for duplicate files, reviewing documentation, reviewing metadata, minting persistent identifiers, and checking for corrupt/broken files. The most "value-add" curation action across generalist, institutional, and disciplinary repository respondents was related to reviewing and enhancing documentation. Respondents reported high perceived impact of curation by their repositories on specific data sharing outcomes including usability, findability, understandability, and accessibility of deposited datasets; respondents associated with disciplinary repositories tended to perceive higher impact on most outcomes. Most survey participants strongly agreed that data curation by the repository adds value to the data sharing process and that it outweighs the effort and cost. We found some differences between institutional and disciplinary repositories, both in the reported frequency of specific curation actions as well as the perceived impact of data curation. Interestingly, we also found variation in the perceptions of those working within the same repository regarding the level and frequency of curation actions performed, which exemplifies the complexity of a repository curation work. Our results suggest data curation may be better understood in terms of specific curation actions and outcomes than broadly defined curation levels and that more research is needed to understand the resource implications of performing these activities. We share these results to provide a more nuanced view of curation, and how curation impacts the broader data lifecycle and data sharing behaviors.

Effectiveness of remote risk-based monitoring and potential benefits for combination with direct data capture.

BACKGROUND: In recent years, alternative monitoring approaches, such as risk-based and remote monitoring techniques, have been recommended instead of traditional on-site monitoring to achieve more efficient monitoring. Remote risk-based monitoring (R2BM) is a monitoring technique that combines risk-based and remote monitoring and focuses on the detection of critical data and process errors. Direct data capture (DDC), which directly collects electronic source data, can facilitate R2BM by minimizing the extent of source documents that must be reviewed and reducing the additional workload on R2BM. In this study, we evaluated the effectiveness of R2BM and the synergistic effect of combining R2BM with DDC. METHODS: R2BM was prospectively conducted with eight participants in a randomized clinical trial using a remote monitoring system that uploaded photographs of source documents to a cloud location. Critical data and processes were verified by R2BM, and later, all were confirmed by on-site monitoring to evaluate the ability of R2BM to detect critical data and process errors and the workload of uploading photographs for clinical trial staff. In addition, the reduction of the number of uploaded photographs was evaluated by assuming that the DDC was introduced for data collection. RESULTS: Of the 4645 data points, 20.9% (n = 973, 95% confidence interval = 19.8-22.2) were identified as critical. All critical data errors corresponding to 5.4% (n = 53/973, 95% confidence interval = 4.1-7.1) of the critical data and critical process errors were detectable by R2BM. The mean number of uploaded photographs and the mean time to upload them per visit per participant were 34.4 ± 11.9 and 26.5 ± 11.8 min (mean ± standard deviation), respectively. When assuming that DDC was introduced for data collection, 45.0% (95% confidence interval = 42.2-47.9) of uploaded photographs for R2BM were reduced. CONCLUSIONS: R2BM can detect 100% of the critical data and process errors without on-site monitoring. Combining R2BM with DDC reduces the workload of R2BM and further improves its efficiency.

Rationale and Study Checklist for Ethical Rejection of Participants on Crowdsourcing Research Platforms.

Online participant recruitment ("crowdsourcing") platforms are increasingly being used for research studies. While such platforms can rapidly provide access to large samples, there are concomitant concerns around data quality. Researchers have studied and demonstrated means to reduce the prevalence of low-quality data from crowdsourcing platforms, but approaches to doing so often involve rejecting work and/or denying payment to participants, which can pose ethical dilemmas. We write this essay as an associate professor and two institutional review board (IRB) directors to provide a perspective on the competing interests of participants/workers and researchers and to propose a checklist of steps that we believe may support workers' agency on the platform and lessen instances of unfair consequences to them while enabling researchers to definitively reject lower-quality work that might otherwise reduce the likelihood of their studies producing true results. We encourage further, explicit discussion of these issues among academics and among IRBs.

Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.

OBJECTIVE: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). MATERIALS AND METHODS: The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. RESULTS: The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. DISCUSSION: The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation. CONCLUSION: The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.

Validation of Multi-State EHR-Based Network for Disease Surveillance (MENDS) Data and Implications for Improving Data Quality and Representativeness.

Introduction: Surveillance modernization efforts emphasize the potential use of electronic health record (EHR) data to inform public health surveillance and prevention. However, EHR data streams vary widely in their completeness, accuracy, and representativeness. Methods: We developed a validation process for the Multi-State EHR-Based Network for Disease Surveillance (MENDS) pilot project to identify and resolve data quality issues that could affect chronic disease prevalence estimates. We examined MENDS validation processes from December 2020 through August 2023 across 5 data-contributing organizations and outlined steps to resolve data quality issues. Results: We identified gaps in the EHR databases of data contributors and in the processes to extract, map, integrate, and analyze their EHR data. Examples of source-data problems included missing data on race and ethnicity and zip codes. Examples of data processing problems included duplicate or missing patient records, lower-than-expected volumes of data, use of multiple fields for a single data type, and implausible values. Conclusion: Validation protocols identified critical errors in both EHR source data and in the processes used to transform these data for analysis. Our experience highlights the value and importance of data validation to improve data quality and the accuracy of surveillance estimates that use EHR data. The validation process and lessons learned can be applied broadly to other EHR-based surveillance efforts.

Sample-Wise and Gene-Wise Comparisons Confirm a Greater Similarity of RNA and Protein Expression Data at the Level of Molecular Pathways and Suggest an Approach for the Data Quality Check in High-Throughput Expression Databases.

Identification of genes and molecular pathways with congruent profiles in the proteomic and transcriptomic datasets may result in the discovery of promising transcriptomic biomarkers that would be more relevant to phenotypic changes. In this study, we conducted comparative analysis of 943 paired RNA and proteomic profiles obtained for the same samples of seven human cancer types from The Cancer Genome Atlas (TCGA) and NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) [two major open human cancer proteomic and transcriptomic databases] that included 15,112 protein-coding genes and 1611 molecular pathways. Overall, our findings demonstrated statistically significant improvement of the congruence between RNA and proteomic profiles when performing analysis at the level of molecular pathways rather than at the level of individual gene products. Transition to the molecular pathway level of data analysis increased the correlation to 0.19-0.57 (Pearson) and 0.14-057 (Spearman), or 2-3-fold for some cancer types. Evaluating the gain of the correlation upon transition to the data analysis the pathway level can be used to refine the omics data by identifying outliers that can be excluded from the comparison of RNA and proteomic profiles. We suggest using sample- and gene-wise correlations for individual genes and molecular pathways as a measure of quality of RNA/protein paired molecular data. We also provide a database of human genes, molecular pathways, and samples related to the correlation between RNA and protein products to facilitate an exploration of new cancer transcriptomic biomarkers and molecular mechanisms at different levels of human gene expression.

Accuracy, potential, and limitations of probabilistic record linkage in identifying deaths by gender identity and sexual orientation in the state of Rio De Janeiro, Brazil.

BACKGROUND: Globally, the counting of deaths based on gender identity and sexual orientation has been a challenge for health systems. In most cases, non-governmental organizations have dedicated themselves to this work. Despite these efforts in generating information, the scarcity of official data presents significant limitations in policy formulation and actions guided by population needs. Therefore, this manuscript aims to evaluate the accuracy, potential, and limits of probabilistic data relationships to yield information on deaths according to gender identity and sexual orientation in the State of Rio de Janeiro. METHODS: This study evaluated the accuracy of the probabilistic record linkage to obtain information on deaths according to gender and sexual orientation. Data from two information systems were used from June 15, 2015 to December 31, 2020. We constructed nine probabilistic data relationship strategies and identified the performance and cutoff points of the best strategy. RESULTS: The best data blocking strategy was established through logical blocks with the first and last names, birthdate, and mother's name in the pairing strategy. With a population base of 80,178 records, 1556 deaths were retrieved. With an area under the curve of 0.979, this strategy presented 93.26% accuracy, 98.46% sensitivity, and 90.04% specificity for the cutoff point ≥ 17.9 of the data relationship score. The adoption of the cutoff point optimized the manual review phase, identifying 2259 (90.04%) of the 2509 false pairs and identifying 1532 (98.46%) of the 1556 true pairs. CONCLUSION: With the identification of possible strategies for determining probabilistic data relationships, the retrieval of information on mortality according to sexual and gender markers has become feasible. Based on information from the daily routine of health services, the formulation of public policies that consider the LGBTQ + population more closely reflects the reality experienced by these population groups.

Engaging the Next Generation in Communication to Address Information Quality.

Information disorder can have adverse consequences on health. While there has been growing attention to health information quality among the general population, there has been less focus on the young adult age group and how their insights and ideas can help to explore the effects and potential interventions to address information quality. Since certain information consumption habits and effects vary among young people, their perspective can provide valuable insights for tackling the increasing issue of misinformation. This Perspective examines past youth involvement efforts to suggest ways to incorporate the youth perspective into improving the quality of health information, particularly through engagement strategies aimed at combating misinformation traits. We then propose a set of five recommendations to advance research to address information disorder, researchers can consider the following steps to engage youth.